Data Handling and Machine Learning (5 cr)
Code: TX00DZ36-3008
General information
- Enrollment
-
28.11.2022 - 05.03.2023
Registration for the implementation has ended.
- Timing
-
13.03.2023 - 07.05.2023
Implementation has ended.
- Number of ECTS credits allocated
- 5 cr
- Mode of delivery
- On-campus
- Unit
- (2019-2024) School of ICT
- Campus
- Myllypurontie 1
- Teaching languages
- Finnish
- Seats
- 0 - 40
- Degree programmes
- Information and Communication Technology
- Teachers
- Juha Kopu
- Vesa Ollikainen
- Course
- TX00DZ36
Implementation has 14 reservations. Total duration of reservations is 42 h 0 min.
Time | Topic | Location |
---|---|---|
Tue 14.03.2023 time 09:00 - 12:00 (3 h 0 min) |
Datan käsittely ja koneoppiminen TX00DZ36-3008 |
MPA5027
Oppimistila
|
Fri 17.03.2023 time 09:00 - 12:00 (3 h 0 min) |
Datan käsittely ja koneoppiminen TX00DZ36-3008 |
MPA5024
Oppimistila
|
Tue 21.03.2023 time 09:00 - 12:00 (3 h 0 min) |
Datan käsittely ja koneoppiminen TX00DZ36-3008 |
MPA5027
Oppimistila
|
Thu 23.03.2023 time 09:00 - 12:00 (3 h 0 min) |
Datan käsittely ja koneoppiminen TX00DZ36-3008 |
MPA5027
Oppimistila
|
Tue 28.03.2023 time 09:00 - 12:00 (3 h 0 min) |
Datan käsittely ja koneoppiminen TX00DZ36-3008 |
MPA5027
Oppimistila
|
Thu 30.03.2023 time 09:00 - 12:00 (3 h 0 min) |
Datan käsittely ja koneoppiminen TX00DZ36-3008 |
MPA5027
Oppimistila
|
Tue 04.04.2023 time 09:00 - 12:00 (3 h 0 min) |
Datan käsittely ja koneoppiminen TX00DZ36-3008 |
MPA5027
Oppimistila
|
Tue 11.04.2023 time 09:00 - 12:00 (3 h 0 min) |
Datan käsittely ja koneoppiminen TX00DZ36-3008 |
MPA5027
Oppimistila
|
Thu 13.04.2023 time 09:00 - 12:00 (3 h 0 min) |
Datan käsittely ja koneoppiminen TX00DZ36-3008 |
MPA5027
Oppimistila
|
Thu 20.04.2023 time 09:00 - 12:00 (3 h 0 min) |
Datan käsittely ja koneoppiminen TX00DZ36-3008 |
MPA5027
Oppimistila
|
Tue 25.04.2023 time 09:00 - 12:00 (3 h 0 min) |
Datan käsittely ja koneoppiminen TX00DZ36-3008 |
MPA5027
Oppimistila
|
Thu 27.04.2023 time 09:00 - 12:00 (3 h 0 min) |
Datan käsittely ja koneoppiminen TX00DZ36-3008 |
MPA5027
Oppimistila
|
Tue 02.05.2023 time 09:00 - 12:00 (3 h 0 min) |
Datan käsittely ja koneoppiminen TX00DZ36-3008 |
MPA5027
Oppimistila
|
Thu 04.05.2023 time 09:00 - 12:00 (3 h 0 min) |
Datan käsittely ja koneoppiminen TX00DZ36-3008 |
MPA5027
Oppimistila
|
Objective
After completion of the course, the student understands the possibilities in data handling, modelling and, particularly, machine learning. The course participants have acquired hands-on experience in data storage, retrieval, and manipulation as well as the methods and tools in machine learning.
Content
- Large volumes of data in ICT business: applicability, models, opportunities, and processes, legislative and ethical constraints.
- Data acquisition and preprocessing.
- Data management solutions.
- Machine learning methods (classification, association analysis, clustering, prediction of numeric values) , their fields of use and applicability.
- Machine learning software.
- Validation and visualisation of results.
- Machine learning in natural language processing.
Evaluation scale
0-5
Assessment criteria, satisfactory (1)
The student has achieved the course objectives fairly. The student will be able to identify, define and use the course subject area’s concepts and models. The student has completed the required learning exercises in minimum requirement level.
Assessment criteria, good (3)
The student has achieved the course objectives well, even though the knowledge and skills need improvement on some areas. The student has completed the required learning exercises in good or satisfactory level. The student is able to define the course concepts and models and is able to justify the analysis.
Assessment criteria, excellent (5)
The student has achieved the objectives of the course with excellent marks. The student master commendably the course subject area’s concepts and models. The student has completed the required learning exercises in good or excellent level. The student is able to make justified and fluent analysis.
Assessment criteria, approved/failed
The student has achieved the course objectives fairly. The student will be able to identify, define and use the course subject area’s concepts and models. The student has completed the required learning exercises in minimum requirement level.